As the pharmaceutical industry tries to embrace the methodologies of quality by design (QbD) provided by the FDA’s process validation (PV) guidance (1) and International Conference on Harmonization (ICH) Q8/Q9/Q10 (2-4), many companies are challenged by the evolving concept of criticality as applied to quality attributes and process parameters. Historically, in biopharmaceutical development, criticality has been a frequently arbitrary categorization between important high-risk attributes or parameters and those that carry little or no risk. This binary designation was usually determined during early development for the purposes of regulatory filings, relying heavily on scientific judgment and limited laboratory studies.

Figure 1: Process validation lifecycle.

With the most recent ICH and FDA guidances endorsing a new paradigm of process validation based more on process understanding and control of parameters and less on product testing, the means of determining criticality has come under greater scrutiny. The FDA guidance points to a lifecycle approach to process validation (see Figure 1). “With a lifecycle approach to process validation that employs risk-based decision making throughout that lifecycle, the perception of criticality as a continuum rather than a binary state is more useful.” The problem is that a practical approach of determining this criticality “continuum” using risk analysis has been left to each company to develop.

This article presents the first part of a practical roadmap that applies scientific knowledge, risk analysis, experimental data, and process monitoring throughout the three phases of the process validation lifecycle to first determine and then refine criticality. In this approach, criticality is used as a risk-based tool to drive control strategies (Stage 1), qualification protocols (Stage 2), and continued process verification (Stage 3). Overall, a clear roadmap for defining, supporting and evolving the criticality of parameters and attributes throughout the process-validation lifecycle will allow pharmaceutical companies to easily embrace the new process-validation paradigm. Furthermore, processes will be more robust and continuous improvement opportunities more easily identified.

In Part I of this series, the author used risk analysis and applied the continuum of criticality to quality attributes during the process-design stage of process validation. After using process knowledge to relate the attributes to each process unit operation, the inputs and outputs of each unit operation were defined to determine process parameters and in-process controls. An initial risk assessment was then completed to determine a preliminary continuum of criticality for process parameters.

In Part II, the preliminary risk levels of process parameters provided the basis of characterization studies based on design of experiments (DOE). Data from these studies were used to confirm the continuum of criticality for process parameters.

In Part III, the control strategy for the process was developed from a design space established from characterization studies. As the process-qualification stage proceeds, the continuum of criticality was used to develop equipment qualification criteria and strategies for process performance qualification. Finally, in the continued-process-verification stage of process validation, criticality was used to determine the frequency of monitoring and analysis.

From binary to continuum
On the surface, deciding whether an attribute or parameter is critical or not may seem clear and simple. After all, data are compared to acceptance criteria in countless decisions regarding clinical trials, experimental studies, qualifications, and product release. Either the acceptance criteria are met, or they are not. Companies that take this familiar path have tried to draw a definitive line between the “critical” and “not critical” sides. Once a decision has been made about criticality, there is no need to look again. It doesn’t help that the guidance documents for industry have been vague on where this criticality threshold lies. The FDA’s PV guidance avoids the issue: “attribute(s) … and parameter(s) … are not categorized with respect to criticality in this guidance” (1).

ICH Q8(R2) provides the following definitions using the term critical:

• Critical process parameter (CPP). A process parameter whose variability has an impact on a critical quality attribute and, therefore, should be monitored or controlled to ensure the process produces the desired quality.
• Critical quality attribute (CQA). A physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality.

This interpretation of CQA is most applicable to in-process and finished-product specification limits, which suggests that these limits must be critical given that they were designed to ensure product quality. During the early stages of process development and design, other quality attributes may be measured that, over the course of development, do not end up as either in-process or finished-product tests in the commercial process. These test results may show little variation and present little to no risk to product quality. In other cases, while process duration or yield is measured, they are not related to the product quality and are, therefore, not CQAs. However, even when defined as critical, not all CQAs have equal impact on safety and effectiveness (3, 5).

The definition for CPP states that a parameter is considered critical when its variability has an impact on a CQA. The amount of impact is not defined, which leads to the question, does even a small impact to a CQA mean that the parameter is critical? It is not difficult to imagine the example of an extreme shift of a process parameter having a minor impact on a CQA, whether measurable or not. Extreme temperatures can destroy many pharmaceutical products; however, if a process inherently cannot produce such temperatures, is temperature still considered to be critical and, therefore, required to be monitored and controlled?

When these definitions are strictly interpreted, some companies find themselves in one of two extremes:

• Every quality attribute is critical (they all ensure product quality); every parameter is critical (product cannot be made without controlling them)
• No parameter is critical because if they are controlled, all quality attributes will pass specifications.

Reality lies somewhere between these extremes. Logic and common sense dictate that additional criteria must be necessary to aid in determining criticality. There is great value in understanding not only if a parameter/attribute is critical (i.e., has an impact), but also how much impact the parameter/attribute has. All companies have limited time and resources; therefore, the focus must be on that which provides the greatest benefit for the effort

By using risk analysis as a means to determine criticality, an opportunity arises to help resolve these potential conflicts. CQAs should be classified based on the potential risks to the patient. CPPs should be separated into those that have substantial impact on the CQAs and those with minor or no impact. The binary yes/no decision transforms into a continuum of criticality ranging from high impact to low impact critical to not critical. As knowledge increases or as improvements are made to a process throughout the lifecycle, risks may be reduced and the level of impact for a CPP can be modified and control strategies adjusted accordingly

The number of levels in the continuum is a matter of choice and the risk analysis method used. Each company must procedurally define what risk tools and risk levels it will use and consistently apply them across similar products. In the following examples, three levels of impact are used for simplicity’s sake. The different levels drive decision-making and action plans throughout the lifecycle.

For CQAs, a continuum of criticality provides a tool to designate particular attributes as the most important to the protection of the patient. For CPPs, a continuum of criticality allows for process control and monitoring strategies to focus where the greatest impact on product quality is achieved.

Quality risk management
Risk is the combination of the probability of occurrence of harm and the severity of that harm (5, 6). The value of risk assessment models is the formalized evaluation criteria that comes from agreed-upon ranking tables. Even though some may argue that the assessment is not quantitative, the benefit derived from framing the evaluation to an agreed upon risk criteria dramatically improves the ability to objectively evaluate the process risk profile. Per ICH Q9, there are two primary principles of quality risk management (3):

• The evaluation of risk to quality should be based on scientific knowledge and ultimately link to the protection of the patient
• The level of effort, formality, and documentation of the quality risk management should be commensurate with the level of the risk.

Formal risk management tools such as failure mode effects analysis (FMEA) or failure mode effects and criticality analysis (FMECA) (7) can be used to provide a structured semi-quantitative summary of risk. For Stage 1, however, often a qualitative risk assessment evaluating low, medium, and high risk is sufficient to distinguish relative differences in risk.

Continuum of CQAs
Prior to the development of a new drug, companies frequently decide and document a therapeutic need in the marketplace for a new pharmaceutical. It is through this effort that the quality and regulatory aspects of the new drug are defined such as the type of dosage form, the target dose, the in-vivo drug availability, and limit of impurities. Current guidance identifies this documentation as the quality target product profile (QTPP). The QTPP provides the basis of the desired quality characteristics of the drug product, taking into account safety and efficacy (i.e., purity, identity, strength, and quality). The QTPP should not be confused with the drug product specification, which created later, is generally a list of specific test methods to perform and their acceptance criteria designed to ensure drug efficacy and safety. The QTPP is an input to these activities whereas the quality attributes and specifications are outputs.

The initial list of quality attributes from the QTPP should be created as early as possible in the development process so that data can be collected from experimental runs. To assign the continuum of criticality to that initial list of quality attributes, knowledge of the severity of the risk of harm to the patient is paramount. This comes from prior knowledge such as early safety trials and scientific principles.

Quality attributes are rated as the highest criticality level because they have a high severity of risk of harm. Severity is the primary criteria for assessing quality attribute criticality because it is unlikely to change as understanding increases over the life-cycle. For example, an impurity may be determined to severely harm the patient (high severity score) if beyond its limit. If its level does not increase in the process or on stability testing, the occurrence score is low and its overall risk to the patient may be low. However, it is still rated as high risk due to its high severity. That severity will not change and as a high-risk CQA, it has to be tested and monitored.
Examples of risk levels for CQAs:

For a quality attribute to be designated as “not critical,” it has to have no risk to the patient (e.g., yield, process duration). Attributes that are not critical to quality are sometimes named process performance attributes to distinguish from quality attributes.

Not all CQAs are tested as part of finished-product testing. Some are tested in-process to define limits such as pH and conductivity. Although frequently designated as “in-process controls,” they are still quality attributes that should be assessed for their criticality. Consideration should be given to the relationship between in-process controls and finished-product CQAs when making this decision. While this is one example of how to assign a continuum of criticality to quality attributes, other examples are also available (8-10).

Cause-and-effect matrix
Once risk levels have been assigned to the CQAs, the next activity is to begin to relate which parts of the process have impact on these attributes. This cause-and-effect analysis breaks the process into its unit operations and conveys its impact on the CQAs. An example of the cause-and-effect matrix for a biologic is given in Table I.

In addition to the matrix, it is important to document the justification for these decisions as part of this analysis. For example, the parameters of the cation- and anion-exchange chromatography processes are expected to have a high impact on impurities because they are designed to remove impurities of different ionic charge than the desired product. With the knowledge of which unit operations have impact on particular CQAs, it is now possible to analyze each process’ inputs and outputs to determine how process parameters affect the CQAs.

Input and output process variables
Each unit operation has both input and output process variables. Process parameters signify process inputs that are directly controllable and can theoretically impact CQAs. Process outputs that are not directly controllable are attributes. When the attribute ensures product quality, it is a CQA. The output of one unit operation can also be the input of the next unit operation. These parameters and attribute designations and their justification should be documented in either a formal process description document, or process flow diagram/drawing. This documentation should also include the scale of each unit operation, equipment and materials required, sampling/monitoring points, test methods, and relevant processing times and storage times/conditions.

The intent of assessing process parameters is to determine how they affect the process variation of CQAs along with their control strategy. Each company should clearly document their methodology for defining and assessing parameters. The following may be considered in making that assessment:

• Raw material attributes are outputs of the release of materials. Critical material attributes (CMA) should be considered along with CPPs as impacting process variability.
• Fixed parameters such as equipment scale, equipment setup, pre-programmed recipes should be documented but are assessed as either low or non-critical.
• Parameters for sterilization processes and cleaning process and the preparation of process intermediates can be included in the primary process assessment. Alternatively, they can be treated as independent processes with their own process parameters, quality attributes, criticality assessments, and process validation.
• Calibration and standardization setting for equipment and instruments are usually not included as process parameters.
• Formulation recipes can be considered fixed parameters (low or not critical); these parameters generally have relatively tight limits, which are justified during formulation development. Such a parameter, which does not vary, cannot impact process variability. An exception to this rule is the case where operators must calculate a quantity based on a variable input such as biological activity; this variable process parameter may lead to process variation.
• Holding/storage times and conditions where no processing occurs should be qualified to show little to no impact on the product. These should be documented and, if these factors are included as process parameters, they are considered low or non-critical.
• Environmental conditions during process (room temperature, humidity), such as holding times, are to have set limits so that they have little to no impact to the process. Process-specific environmental conditions such as cleanrooms, cold rooms, and dry rooms are included as process parameters because they are monitored to ensure product quality.

When a process parameter is determined to be non-critical either by process knowledge or by process study, companies may choose to further designate the parameter as a key performance parameter if that parameter impacts a process performance attribute.

From knowledge to risks
Once each unit operation is related to CQAs through a cause-and-effect matrix and the process parameters and attributes are documented, an initial risk assessment to determine the potential impact of each process parameter is performed. Prior to process characterization experiments, this risk assessment may be more high level using primarily prior knowledge and scientific principles. However, a more formal FEMCA may also be considered.

Table II is an example of an initial risk assessment for a single unit operation. Included in the justification is the expected relationship with CQAs and how the parameter may be influenced during scale-up. Fixed parameters are set to non-critical as they do not impact process variability. For the initial process characterization experiments, process parameters with medium to high impact will be included.

Table II: Example of initial risk assessment of process parameters

Process parameters

Initialrisk assessment

Justification

Inoculum in-vitro cell age

Low

Separate end of production studies have justified limit of cell age.

Osmolality

Medium

Can affect impurities and cell viability.

Keep constant in scale-up.

Antifoam concentration

No

Knowledge from previous studies have defined acceptable range to have no impact to quality.

Keep constant in scale-up.

Nutrient concentration

Medium

Must be sufficient to maintain cell viability.

Keep constant in scale-up.

Medium storage time and temperature

No

Knowledge of medium storage from previous studies.

No effect when kept within pre-established limits.

Medium expiration (age)

No

No effect when kept within pre-established limits.

Volume of feed addition

Medium

Related to component concentration.

Scale by fermentor volume.

Component concentration in feed

Medium

Yield impact and impacts cell viability.

Related to volume of feed addition.

Keep constant in scale-up.

Amount of glucose

Low

Glucose fed as needed to maintain cell viability. leading to different cell concentrations.

Scale by fermentor volume.

Dissolved oxygen

High

Must be sufficient to maintain cell viability.

Impacts yield by low cell growth.

Controlled by rate of aeration.

Scale to large scale by pre-defined model.

Temperature

High

Impacts cell growth and viability.

Keep constant on scale up.

pH

High

Impacts cell growth and viability.

Keep constant on scale up.

Agitation rate

Low

Speed set by previous process experience.

Scale to large scale by pre-defined models.

Culture duration (days)

High

Related to nutrient concentration for cell viability.

In Part I of this series, the author looked at criticality as a continuum to apply risk analysis during process design, and to relate process unit operations to quality attributes using a cause-and-effect matrix.

In Part II, the continuum of criticality for parameter and attributes will be used to design process characterization studies using DOE. From the initial risk assessment of critical parameters, experimental data from formal studies will confirm the criticality assignment—critical or not—and help to assess the level of impact to CQAs.